In natural rainy scenes, visibility is significantly degraded by two types of phenomena: specular highlights of nearby individual rain streaks and atmospheric veiling effect caused by distant accumulated rain. However, most existing deraining methods only take the first kind of degradation into consideration, which limits their potential application in heavy rain. In this study, a joint rain and atmospheric veil removal framework is proposed to address this problem. Since rain streaks and rain accumulation are entangled with each other, which is intractable to simulate, causing clean/rainy image pairs of real-world are hard to generate. Hence, after introducing a generalised rain model, which can represent both rain streaks and atmospheric veil physically, the authors do not learn the mapping function between image pairs using deep-learning architecture, but estimate the rain streaks, transmission, and atmospheric light via Gaussian mixture model patch prior and dark channel prior to solve the rain model instead. According to the comprehensive experimental evaluations, the proposed method outperforms other state-of-the-art methods in terms of both high visibility and vivid colour, especially in natural heavy rain scenario.

中文翻译：

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从单个图像中去除联合降雨和大气面纱

在自然的雨天场景中，能见度会因两种现象而大大降低：附近个别雨条纹的镜面高光和远处累积的雨水造成的大气遮盖作用。然而，大多数现有的排水方法仅考虑第一类退化，这限制了它们在大雨中的潜在应用。在这项研究中，提出了一个联合的雨水和大气面纱清除框架来解决这个问题。由于降雨条纹和降雨积聚相互缠结，这很难模拟，导致难以生成真实世界的干净/多雨图像对。因此，在引入广义的降雨模型之后，该模型可以物理上代表降雨条纹和大气面纱，作者没有使用深度学习架构来学习图像对之间的映射功能，而是通过先于高斯混合模型补丁和暗通道来估计降雨条纹，透射率和大气光，然后再求解降雨模型。根据全面的实验评估，该方法在可见度高和色彩鲜艳方面均优于其他最新方法，尤其是在自然大雨情况下。